Objective: The knowledge, attitudes, and practices of SUA students about the use of plastics containing endocrine disruptors were investigated in this study. Methodology: A study with 150 participants was conducted to assess individuals' knowledge about endocrine disruptors, attitudes, and plastic use practices. Results: The findings indicate that the participants possessed an average degree of knowledge 50.2 ± 3.85 with the main emphasis of awareness being generic concepts rather than specific substances. Regarding the potential health impacts of endocrine-disrupting chemicals present in plastics, respondents' attitudes ranged from fair to positive, with a mean score of 3.5 ±0.09 indicating a fair attitude overall. Conclusion: It is important to practice polite behavior and increase public awareness of safe plastic disposal methods. Surprising only 38.0% of the participants mentioned that they refrain from heating their food in plastic containers to reduce their exposure to plastics. Students' practices revealed a notable dependence on plastic products despite their awareness of the concerns surrounding endocrine disruptors, as most of them reported using plastic water bottles, plastic cups, and plastic bags almost always. Additionally, only 20.7% of the respondents consistently implemented strategies to prevent exposure to endocrine-disrupting chemicals. Recommendation: The study recommended increasing the use of cleaner plastic substitutes and improving educational programs to convert information into practical actions. Policies that encourage environmentally friendly behavior and raise public awareness of safe plastic disposal techniques should be put into practice.

The integration of LiDAR and Artificial Intelligence (AI) has revolutionized feature detection in urban environments. LiDAR systems, which utilize pulsed laser emissions and reflection measurements, produce detailed 3D maps of urban landscapes. When combined with AI, this data enables accurate identification of urban features such as buildings, green spaces, and infrastructure. This synergy is crucial for enhancing urban development, environmental monitoring, and advancing smart city governance. LiDAR, known for its high-resolution 3D data capture capabilities, paired with AI, particularly deep learning algorithms, facilitates advanced analysis and interpretation of urban areas. This combination supports precise mapping, real-time monitoring, and predictive modeling of urban growth and infrastructure. For instance, AI can process LiDAR data to identify patterns and anomalies, aiding in traffic management, environmental oversight, and infrastructure maintenance. These advancements not only improve urban living conditions but also contribute to sustainable development by optimizing resource use and reducing environmental impacts. Furthermore, AI-enhanced LiDAR is pivotal in advancing autonomous navigation and sophisticated spatial analysis, marking a significant step forward in urban management and evaluation. The reviewed paper highlights the geometric properties of LiDAR data, derived from spatial point positioning, and underscores the effectiveness of machine learning algorithms in object extraction from point clouds. The study also covers concepts related to LiDAR imaging, feature selection methods, and the identification of outliers in LiDAR point clouds. Findings demonstrate that AI algorithms, especially deep learning models, excel in analyzing high-resolution 3D LiDAR data for accurate urban feature identification and classification. These models leverage extensive datasets to detect patterns and anomalies, improving the detection of buildings, roads, vegetation, and other elements. Automating feature extraction with AI minimizes the need for manual analysis, thereby enhancing urban planning and management efficiency. Additionally, AI methods continually improve with more data, leading to increasingly precise feature detection. The results indicate that the pulse emitted by continuous wave LiDAR sensors changes when encountering obstacles, causing discrepancies in measured physical parameters.

The Douala-Edea National Park is a coastal protected area that opens to the Atlantic Ocean, and contains an abundant wildlife which finds a privilege habitat there, and certain taxa such as epiphytes, which are of particular interest for conservation. In many tropical forests, vascular epiphytes are one of the richest taxa, with major impacts on the nutrient and hydrological cycles. The aim of this research was to study the effect of the disturbance of habitat on the floristic composition of vascular epiphytes in the Douala-Edea National Park. This study was carried out between January - April 2021 in three types of disturbed habitats at the northern part of the park. Three plots of 100 m × 100 m dimensions were laid out across three ecosystems along the Sanaga river. The sampling method consisted in the direct observation of five adjacent transects of 100 m x 20 m dimensions inside each plot. Epiphytes species were evaluated on all trees of DBH ≥10 cm. Epiphytes' life-forms and the position on the host trees occupied by the epiphytes were also recorded. A total of 18 species belonging to 16 genera and 13 families were identified. Culcasia sp. was the most common species with a relative frequency of 30.27%. Biological indicators were represented by Ferns, with four species, and Orchidaceae, with one species. The epiphytes species richness was highest in the low disturbed habitat (13 species), and lowest in the highly disturbed habitat (8 species). Strict epiphytes were highly recorded in the low disturbed habitat (6 species), and were absent in the highly disturbed habitat. Hemi-epiphytes were the commonest life-form (12 species) in the highly disturbed habitat, and have been defined as indicators of the perturbation of the habitat. Canopy was mostly sollicitated by epiphytes in the low disturbed habitat (66.25%) than the moderate disturbed habitat (49.85%), and highly disturbed habitat (30.66%). It has been found that the different forest sites have an influence on the typology of epiphytic species, and therefore, epiphytic flora should be managed for the conservation of the biodiversity in tropical forests.

Background: The COVID-19 pandemic disrupted routine osteoporosis care due to clinic closures and limited in-person consultations. Telehealth emerged as an alternative model enabling remote care delivery and monitoring. However, previous reviews on telehealth either did not include the pandemic period or had a limited focus in scope. Evidence synthesized specifically for osteoporosis care during the pandemic is needed but lacking. Methods: We systematically searched PubMed, MEDLINE, EMBASE, PsycINFO, Web of Science, and CINAHL for studies on telehealth for osteoporosis published between January 2021 and March 2023. Five studies met the inclusion criteria of: osteoporosis population, telehealth intervention, and COVID-19 pandemic timeframe. Data was extracted on study characteristics, COVID-19 outcomes, osteoporosis status, telehealth purpose, patient satisfaction, and clinical outcomes. Result: The five studies showed telehealth was used for monitoring data, delivering test results, adjusting medications, and assessments. Osteoporosis prevalence among telehealth users ranged 30-100%. High patient satisfaction was reported with telehealth versus in-person care. No major differences occurred in medication delays or fractures between telehealth and in-person groups. Conclusion: This review found telehealth enables effective osteoporosis care and monitoring during the pandemic, with high patient and provider satisfaction. However, more robust randomized controlled trials are needed to establish stronger evidence around telehealth's impacts on clinical osteoporosis outcomes. Implications: Though promising, further high-quality studies will help clarify telehealth's role in improving osteoporosis care and outcomes. Findings inform guidelines on integrating telehealth into routine management. Evidence on user perspectives optimizes telehealth implementation policies.